Social Selectivity in Higher Education: A Case Study of Canada and the Czech Republic
Bibliographic record
Abstract
This work examines the choices of individuals with respect to higher education in the Czech Republic and Canada. Specifically, how do the students’ socioeconomic backgrounds influence their study decisions. Data from the Czech edition of The European Union Statistics on Income and Living Conditions survey and the Canadian Survey of Labour and Income Dynamics was used to identify influences of students’ entry to university. Individuals from households with higher socioeconomic status were more likely to enter university than people who were less well off in both countries. Social selectivity is much more present in higher education in the Czech Republic than in Canada. Key words: Higher Education, Czech Republic, Canada, Social Selectivity, Logistic Regression, Ordered Statistics Ce travail examine les choix des personnes en matière d'enseignement supérieur en République tchèque et au Canada, notamment comment les antécédents socio-économiques des étudiants influencent leurs décisions relatives aux études. Les données de l'édition tchèque de l'enquête Statistiques sur le revenu et les conditions de vie de l'Union européenne et de l'Enquête canadienne sur la dynamique du travail et du revenu ont été utilisées pour identifier les influences sur l'entrée des étudiants à l'université. Dans les deux pays, les personnes issues de ménages ayant un statut socio-économique élevé étaient plus susceptibles d'entrer à l'université que les personnes moins bien loties. La sélectivité sociale est beaucoup plus présente dans l'enseignement supérieur en République tchèque qu'au Canada. Mots clés: enseignement supérieur, République tchèque, Canada, sélectivité sociale, régression logistique, statistiques ordonnées
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".